Generalized relevance learning vector quantization
Neural Networks - New developments in self-organizing maps
Supervised Neural Gas with General Similarity Measure
Neural Processing Letters
Estimating optimal feature subsets using efficient estimation of high-dimensional mutual information
IEEE Transactions on Neural Networks
Energy Supervised Relevance Neural Gas for Feature Ranking
Neural Processing Letters
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We describe a kernel method which uses the maximization of Onicescu's informational energy as a criteria for computing the relevances of input features. This adaptive relevance determination is used in combination with the neural-gas and the generalized relevance LVQ algorithms. Our quadratic optimization function, as an L2 type method, leads to linear gradient and thus easier computation. We obtain an approximation formula similar to the mutual information based method, but in a more simple way.